The subject matter disclosed herein generally relates to electrical machines. More specifically, the subject matter relate to methods and systems for detecting potential faults and prediction of End of Life (EoL) of electrical machines.
Electric machines such as generators and motors are subjected to failure due to factors such as aging, severe operating conditions and hostile environments. Downtime caused by an unexpected failure of electric machines reduces productivity and profitability.
Rectifying faults and replacing the machines before the failure during a planned maintenance schedule is preferred, but leads to increased costs when machines are replaced or repaired prematurely. Access to historical repair information allows an understanding of the conditional failure probabilities of components of electrical machines. But, failure patterns vary significantly from the published data depending on the ratings and other attributes related to the electrical machines. Failures are also influenced by the specific operating parameters and environment such that generalized data is not highly illustrative. According to industry standards, almost one half of the total failures for electric motors are bearing-related failures. Additionally, one third or more failures are typically related to the winding insulation and iron core failures.
There is a need for an enhanced system and method for detecting potential faults and predict EoL in electrical machines.